Corrected graphs to use common params

Author: epage480

scrapegraphai/graphs/csv_scraper_graph.py

@@ -36,14 +36,24 @@ def _create_graph(self):
         parse_node = ParseNode(
             input="doc",
             output=["parsed_doc"],
+            node_config={
+                "chunk_size": self.model_token,
+            }
         )
         rag_node = RAGNode(
             input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
+            node_config={
+                "llm_model": self.llm_model,
+                "embedder_model": self.embedder_model,
+            }
         )
         generate_answer_node = GenerateAnswerCSVNode(
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
+            node_config={
+                "llm_model": self.llm_model,
+            }
         )
 
         return BaseGraph(
@@ -68,4 +78,4 @@ def run(self) -> str:
         inputs = {"user_prompt": self.prompt, self.input_key: self.source}
         self.final_state, self.execution_info = self.graph.execute(inputs)
 
-        return self.final_state.get("answer", "No answer found.")
+        return self.final_state.get("answer", "No answer found.")

scrapegraphai/graphs/json_scraper_graph.py

@@ -61,16 +61,23 @@ def _create_graph(self) -> BaseGraph:
             input="doc",
             output=["parsed_doc"],
             node_config={
-                "chunk_size": self.model_token,
+                "chunk_size": self.model_token
             }
         )
         rag_node = RAGNode(
             input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
+            node_config={
+                "llm_model": self.llm_model,
+                "embedder_model": self.embedder_model
+            }
         )
         generate_answer_node = GenerateAnswerNode(
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
+            node_config={
+                "llm": self.llm_model
+            }
         )
 
         return BaseGraph(
@@ -99,4 +106,4 @@ def run(self) -> str:
         inputs = {"user_prompt": self.prompt, self.input_key: self.source}
         self.final_state, self.execution_info = self.graph.execute(inputs)
 
-        return self.final_state.get("answer", "No answer found.")
+        return self.final_state.get("answer", "No answer found.")

scrapegraphai/graphs/script_creator_graph.py

@@ -71,10 +71,15 @@ def _create_graph(self) -> BaseGraph:
         rag_node = RAGNode(
             input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
+            node_config={
+                "llm_model": self.llm_model,
+                "embedder_model": self.embedder_model
+            }
         )
         generate_scraper_node = GenerateScraperNode(
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
+            node_config={"llm_model": self.llm_model},
             library=self.library,
             website=self.source
         )
@@ -105,4 +110,4 @@ def run(self) -> str:
         inputs = {"user_prompt": self.prompt, self.input_key: self.source}
         self.final_state, self.execution_info = self.graph.execute(inputs)
 
-        return self.final_state.get("answer", "No answer found.")
+        return self.final_state.get("answer", "No answer found.")

scrapegraphai/graphs/search_graph.py

@@ -49,25 +49,35 @@ def _create_graph(self) -> BaseGraph:
         search_internet_node = SearchInternetNode(
             input="user_prompt",
             output=["url"],
+            node_config={
+                "llm_model": self.llm_model
+            }
         )
         fetch_node = FetchNode(
             input="url | local_dir",
-            output=["doc"],
+            output=["doc"]
         )
         parse_node = ParseNode(
             input="doc",
             output=["parsed_doc"],
             node_config={
-                "chunk_size": self.model_token,
+                "chunk_size": self.model_token
             }
         )
         rag_node = RAGNode(
             input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
+            node_config={
+                "llm_model": self.llm_model,
+                "embedder_model": self.embedder_model
+            }
         )
         generate_answer_node = GenerateAnswerNode(
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
+            node_config={
+                "llm_model": self.llm_model
+            }
         )
 
         return BaseGraph(
@@ -98,4 +108,4 @@ def run(self) -> str:
         inputs = {"user_prompt": self.prompt}
         self.final_state, self.execution_info = self.graph.execute(inputs)
 
-        return self.final_state.get("answer", "No answer found.")
+        return self.final_state.get("answer", "No answer found.")

scrapegraphai/graphs/smart_scraper_graph.py

@@ -57,22 +57,29 @@ def _create_graph(self) -> BaseGraph:
         """
         fetch_node = FetchNode(
             input="url | local_dir",
-            output=["doc"],
+            output=["doc"]
         )
         parse_node = ParseNode(
             input="doc",
             output=["parsed_doc"],
             node_config={
-                "chunk_size": self.model_token,
+                "chunk_size": self.model_token
             }
         )
         rag_node = RAGNode(
             input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
+            node_config={
+                "llm_model": self.llm_model,
+                "embedder_model": self.embedder_model
+            }
         )
         generate_answer_node = GenerateAnswerNode(
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
+            node_config={
+                "llm_model": self.llm_model
+            }
         )
 
         return BaseGraph(
@@ -101,4 +108,4 @@ def run(self) -> str:
         inputs = {"user_prompt": self.prompt, self.input_key: self.source}
         self.final_state, self.execution_info = self.graph.execute(inputs)
 
-        return self.final_state.get("answer", "No answer found.")
+        return self.final_state.get("answer", "No answer found.")

scrapegraphai/graphs/speech_graph.py

@@ -56,28 +56,34 @@ def _create_graph(self) -> BaseGraph:
 
         fetch_node = FetchNode(
             input="url | local_dir",
-            output=["doc"],
+            output=["doc"]
         )
         parse_node = ParseNode(
             input="doc",
             output=["parsed_doc"],
             node_config={
-                "chunk_size": self.model_token,
+                "chunk_size": self.model_token
             }
         )
         rag_node = RAGNode(
             input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
+            node_config={
+                "llm_model": self.llm_model,
+                "embedder_model": self.embedder_model            }
         )
         generate_answer_node = GenerateAnswerNode(
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
+            node_config={
+                "llm_model": self.llm_model
+            }
         )
         text_to_speech_node = TextToSpeechNode(
             input="answer",
             output=["audio"],
             node_config={
-                "tts_model": OpenAITextToSpeech(self.config["tts_model"]),
+                "tts_model": OpenAITextToSpeech(self.config["tts_model"])
             }
         )
 
@@ -116,4 +122,4 @@ def run(self) -> str:
             "output_path", "output.mp3"))
         print(f"Audio saved to {self.config.get('output_path', 'output.mp3')}")
 
-        return self.final_state.get("answer", "No answer found.")
+        return self.final_state.get("answer", "No answer found.")

scrapegraphai/graphs/xml_scraper_graph.py

@@ -57,22 +57,29 @@ def _create_graph(self) -> BaseGraph:
 
         fetch_node = FetchNode(
             input="xml_dir",
-            output=["doc"],
+            output=["doc"]
         )
         parse_node = ParseNode(
             input="doc",
             output=["parsed_doc"],
             node_config={
-                "chunk_size": self.model_token,
+                "chunk_size": self.model_token
             }
         )
         rag_node = RAGNode(
             input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
+            node_config={
+                "llm_model": self.llm_model,
+                "embedder_model": self.embedder_model
+            }
         )
         generate_answer_node = GenerateAnswerNode(
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
+            node_config={
+                "llm_model": self.llm_model
+            }
         )
 
         return BaseGraph(
@@ -101,4 +108,4 @@ def run(self) -> str:
         inputs = {"user_prompt": self.prompt, self.input_key: self.source}
         self.final_state, self.execution_info = self.graph.execute(inputs)
 
-        return self.final_state.get("answer", "No answer found.")
+        return self.final_state.get("answer", "No answer found.")